Registration is an essential problem in image-guided surgery (IGS) since it brings different involved coordinate frames together. Nonrigid or deformable registration still faces many challenges, such as two point sets (PSs) are partially overlapped. To tackle the challenges in the nonrigid registration, we introduce a new two-step point-based registration pipeline that includes two steps. In the first step, the rigid transformation between the two spaces is recovered where the orientation vectors are adopted. In the second step, built on the nonrigid coherent point drift (CPD) approach, the anisotropic positional noise is also assumed. Registration results on the human liver verify the proposed approach’ great improvements over the other methods. First, the rotation and translation are recovered with smaller error values than the existing methods. Second, our registration method’s performance is much more robust to the partial overlapping between two PSs. Third, the two-step registration framework achieves the best performances in most test cases when there is a localization error in acquiring the intraoperative data. Note to Practitioners —A novel registration approach is presented for image-guided liver surgery (LGLS). Compared with existing nonrigid registration methods, two significant changes (or improvements) exist in the proposed registration framework: 1) the normal vectors are extracted and utilized in the rigid registration step and 2) the anisotropic positional uncertainties are considered. In both steps, the registration problems are formulated as a maximum likelihood (ML) problems and dealt with the expectation-maximization (EM) technique. In both steps, the matrix form of the updated positional covariance is provided and can speed up the computational process. The readers are reminded that with extra information and a more general positional error assumption, our approach demonstrates improved performances in the case of partial-to-full alignment.

中文翻译：

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图像引导肝脏手术的特征引导非刚性3D点集配准框架：从各向同性位置噪声到各向异性位置噪声

配准是图像引导手术（IGS）的一个基本问题，因为它可以将不同的相关坐标框架组合在一起。非刚性或可变形配准仍然面临许多挑战，例如两个点集（PSs）部分重叠。为了解决非刚性注册中的挑战，我们引入了一个新的两步基于点的注册管道，该管道包括两个步骤。第一步，恢复两个空间之间的刚性变换，并采用方向矢量。在第二步中，基于非刚性相干点漂移（CPD）方法，还假定了各向异性的位置噪声。在人体肝脏上的注册结果证明了该方法相对于其他方法的巨大改进。首先，与现有方法相比，以较小的误差值恢复了旋转和平移。其次，我们的注册方法的性能对于两个PS之间的部分重叠要更健壮。第三，当在获取术中数据时存在本地化错误时，两步注册框架可在大多数测试案例中获得最佳性能。执业者须知 —提出了一种用于图像引导肝脏手术（LGLS）的新颖注册方法。与现有的非刚性配准方法相比，建议的配准框架存在两个重大变化（或改进）：1）在刚性配准步骤中提取并利用了法向矢量； 2）考虑了各向异性的位置不确定性。在这两个步骤中，都将配准问题表述为最大似然（ML）问题，并使用期望最大化（EM）技术进行处理。在这两个步骤中，提供了更新后的位置协方差的矩阵形式，并且可以加快计算过程。提醒读者，通过提供更多信息和更一般的位置误差假设，我们的方法证明了在部分对准完全对准的情况下，性能得到了改善。